Predicting the Future Is Possible. ‘Superforecasters’ Know How.
Can we predict the future more accurately?
It’s a question we humans have grappled with since the dawn of civilization — one that has massive implications for how we run our organizations, how we make policy decisions, and how we live our everyday lives.
It’s also the question that Philip Tetlock, a psychologist at the University of Pennsylvania and a co-author of “Superforecasting: The Art and Science of Prediction,” has dedicated his career to answering. In 2011, he recruited and trained a team of ordinary citizens to compete in a forecasting tournament sponsored by the U.S. intelligence community. Participants were asked to place numerical probabilities from 0 to 100 percent on questions like “Will North Korea launch a new multistage missile in the next year” and “Is Greece going to leave the eurozone in the next six months?” Tetlock’s group of amateur forecasters would go head-to-head against teams of academics as well as career intelligence analysts, including those from the C.I.A., who had access to classified information that Tetlock’s team didn’t have.
The results were shocking, even to Tetlock. His team won the competition by such a large margin that the government agency funding the competition decided to kick everyone else out, and just study Tetlock’s forecasters — the best of whom were dubbed “superforecasters” — to see what intelligence experts might learn from them.
So this conversation is about why some people, like Tetlock’s “superforecasters,” are so much better at predicting the future than everyone else — and about the intellectual virtues, habits of mind, and ways of thinking that the rest of us can learn to become better forecasters ourselves. It also explores Tetlock’s famous finding that the average expert is roughly as accurate as “a dart-throwing chimpanzee” at predicting future events, the inverse correlation between a person’s fame and their ability to make accurate predictions, how superforecasters approach real-life questions like whether robots will replace white-collar workers, why government bureaucracies are often resistant to adopt the tools of superforecasting and more.